1. Field Invention
This invention relates generally to real estate appraisals and sales price predictions. In particular, the invention relates to an automated real estate appraisal system and method that uses predictive modeling to perform pattern recognition and classification in order to provide accurate sales price predictions.
2. Description of Related Art
The "appraised value" of a real estate parcel, or property, comprises some estimate of the full market value of the property on a specified date. A property's appraised value is of great importance in many types of real estate transactions, including sales and loans.
Conventionally, appraised value is determined by a professional appraiser using both objective and subjective factors. One disadvantage of such a method is the difficulty in ensuring that the appraiser conducts a neutral, unbiased analysis in arriving at the appraised value. This difficulty is often compounded by the fact that the appraiser may be retained and paid by an interested party in the contemplated transaction, such as a lender, mortgage broker, buyer, or seller.
In order to reduce bias and provide more accurate appraisals, statistical techniques may be used to obtain an independent, consistent, mathematically derived estimate of a property's value to assist an appraiser in generating an appraised value. Traditional statistical techniques, such as multiple linear regression and logistic regression, have been tried, but such techniques typically suffer from a number of deficiencies. One deficiency is the inability of traditional regression models to capture complex behavior in predictor variables resulting from nonlinearities and interactions among predictor variables. In addition, traditional regression models do not adapt well to changing trends in the data, so that automated model redevelopment is difficult to implement.
One example of the difficulty of applying a regression model to appraisal problems is the uncertainty as to the optimal temporal and geographical sample size for model development. A model developed using all homes in one square city block might theoretically be an effective predictor for that particular neighborhood, but it may not be possible to develop such a model with sufficient stability and reliability, due to the relatively small sample size. On the other hand, a model developed using all homes sold in the United States in the past month might have a sufficiently large sample size, but might be unable to capture local, neighborhood characteristics to provide an accurate appraisal. Thus, a significant deficiency of traditional regression modeling techniques when applied to real estate appraisals is the inability to successfully model neighborhood characteristics while including a sufficiently large sample size to develop a robust, stable statistical model.
It is desirable, therefore, to have an automated system that uses available information regarding real estate properties to provide accurate estimates of value. Preferably, such a system should be flexible enough to allow model development in a relatively small geographic area, it should be able to handle nonlinearities and interactions among predictor variables without advance specification, it should have high predictive accuracy, and it should have capability for redevelopment of the underlying system model as new patterns of real estate pricing emerge.